Modeling Latent Information in Voting Data with Dirichlet Process Priors.

Citation:

Traunmuller, Richard, Andreas Murr, and Jeff Gill. “Modeling Latent Information in Voting Data with Dirichlet Process Priors.” Political Analysis 23, no. 1 (2015): 1-20.

Abstract:

We apply a specialized Bayesian method that helps us deal with the methodological challenge of unobserved
heterogeneity among immigrant voters. Our approach is based on \emph{generalized linear mixed Dirichlet models} (GLMDM) where
random effects are specified semiparametrically using a Dirichlet process mixture prior that has been shown to account for
unobserved grouping in the data. Such models are drawn from Bayesian nonparametrics to help overcome objections handling latent
effects with strongly informed prior distributions. Using 2009 German voting data of immigrants, we show that for difficult
problems of missing key covariates and unexplained heterogeneity this approach provides (1) overall improved model fit, (2)
smaller standard errors on average, and (3) less bias from omitted variables. As a result, the GLMDM changed our substantive
understanding of the factors affecting immigrants' turnout and vote choice. Once we account for unobserved heterogeneity among
immigrant voters, whether a voter belongs to the first immigrant generation or not is much less important than the extant
literature suggests.  When looking at vote choice we also found that an immigrant's degree of structural integration does not
affect the vote in favor of the CDU/CSU, a party which is traditionally associated with restrictive immigration policy.

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